In recent years, control systems able to predict the continuous adjustments of dynamic factors, which allow the adaptability in the building energy management, have become necessary due to the increasing complexity of HVAC systems, and the rapid change in occupants' behaviour. Classic control systems, including On/Off or PID, can not perform these tasks because they do not provide any prediction capabilities. Moreover, model-based predictive control strategies, such as Model Predictive Control (MPC), are complex to apply because they both need a model for the optimisation, which is difficult to achieve and have a high computational cost. For these reasons, recent researches are focusing on model-free control strategies, and in particular on the application of Reinforcement Learning (RL).